feat(session): 会话存储体系实现 & Chat多Agent路由

- 新增诊断会话(diagnosis_session/agent_step/tool_invocation)三表 - AgentLoggingHook 持久化 agent_step，记录决策链和耗时 - LookupKnowledgeTool 写入 tool_invocation，记录L0/L1检索质量 - TokenTrackingChatModel 捕获真实token用量 - Chat接口支持意图路由：简单问题单Agent，复杂问题多Agent(Planner+Executor) - Prompt外置到 src/main/resources/prompts/ - 删除旧 diagnosis_record 表及相关文件 - 新增SessionContextHolder(ThreadLocal传递sessionId) - QuestionComplexity 复杂度判断工具 - 测试覆盖三张新表的Repository
2026-06-26 16:22:05 +08:00
parent a74ccea5be
commit 0d9cce75f9
33 changed files with 1941 additions and 470 deletions
@@ -1,24 +1,40 @@
 package com.superbiz.agent.service;

+import com.alibaba.cloud.ai.graph.OverAllState;
 import com.alibaba.cloud.ai.graph.agent.ReactAgent;
+import com.alibaba.cloud.ai.graph.agent.flow.agent.SupervisorAgent;
 import com.alibaba.cloud.ai.graph.exception.GraphRunnerException;
 import com.superbiz.agent.agent.tool.DateTimeTools;
 import com.superbiz.agent.agent.tool.InternalDocsTools;
 import com.superbiz.agent.agent.tool.QueryLogsTools;
 import com.superbiz.agent.agent.tool.QueryMetricsTools;
-import com.superbiz.agent.tool.LookupKnowledgeTool;
+import com.superbiz.agent.domain.entity.DiagnosisSession;
 import com.superbiz.agent.hook.AgentLoggingHook;
+import com.superbiz.agent.hook.TokenTrackingChatModel;
+import com.superbiz.agent.hook.TokenUsageHolder;
+import com.superbiz.agent.repository.AgentStepRepository;
+import com.superbiz.agent.repository.DiagnosisSessionRepository;
+import com.superbiz.agent.tool.LookupKnowledgeTool;
+import com.superbiz.agent.util.QuestionComplexity;
+import com.superbiz.agent.util.SessionContextHolder;

+import jakarta.annotation.PostConstruct;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.ai.chat.messages.AssistantMessage;
 import org.springframework.ai.chat.model.ChatModel;
 import org.springframework.ai.tool.ToolCallback;
 import org.springframework.ai.tool.ToolCallbackProvider;
 import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.core.io.ClassPathResource;
 import org.springframework.stereotype.Service;

+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
 import java.util.List;
 import java.util.Map;
+import java.util.Optional;
+import java.util.UUID;

 /**
 * 聊天服务
@@ -50,6 +66,37 @@ public class ChatService {
    @Autowired
    private LookupKnowledgeTool lookupKnowledgeTool;

+    @Autowired
+    private DiagnosisSessionRepository diagnosisSessionRepository;
+
+    @Autowired
+    private AgentStepRepository agentStepRepository;
+
+    /** 多 Agent Chat 的 Prompt */
+    private String chatPlannerPrompt;
+    private String chatExecutorPrompt;
+
+    @PostConstruct
+    public void init() {
+        // 加载 Prompt
+        try {
+            chatPlannerPrompt = new String(
+                new ClassPathResource("prompts/chat-planner-prompt.md").getInputStream().readAllBytes(),
+                StandardCharsets.UTF_8);
+            chatExecutorPrompt = new String(
+                new ClassPathResource("prompts/chat-executor-prompt.md").getInputStream().readAllBytes(),
+                StandardCharsets.UTF_8);
+            logger.info("Chat 多 Agent Prompts 加载成功");
+        } catch (IOException e) {
+            logger.error("加载 Chat Prompt 文件失败", e);
+            throw new RuntimeException("Failed to load chat prompts", e);
+        }
+
+        // 包装 ChatModel 以捕获 token 用量
+        chatModel = new TokenTrackingChatModel(chatModel);
+        logger.info("ChatModel 已包装 TokenTrackingChatModel");
+    }
+
    /**
     * 获取注入的 ChatModel
     */
@@ -177,7 +224,7 @@ public class ChatService {
                .systemPrompt(systemPrompt)
                .methodTools(buildMethodToolsArray())
                .tools(getToolCallbacks())
-                .hooks(new AgentLoggingHook())  // 添加日志 Hook
+                .hooks(new AgentLoggingHook(agentStepRepository, "intelligent_assistant"))
                .build();
    }

@@ -191,16 +238,201 @@ public class ChatService {
        logger.info("========================================");
        logger.info("📝 用户问题: {}", question);

+        String sessionId = UUID.randomUUID().toString().substring(0, 8);
        long startTime = System.currentTimeMillis();
-        var response = agent.call(question);
-        long duration = System.currentTimeMillis() - startTime;

-        String answer = response.getText();
+        // 创建诊断会话
+        DiagnosisSession session = DiagnosisSession.builder()
+                .sessionId(sessionId)
+                .query(question)
+                .status("RUNNING")
+                .agentFlow("CHAT")
+                .build();
+        diagnosisSessionRepository.save(session);

-        logger.info("⏱️  总耗时: {} ms", duration);
-        logger.info("📏 输出长度: {} 字符", answer.length());
-        logger.info("========================================");
+        // 设置 ThreadLocal 上下文（LookupKnowledgeTool 通过此获取 sessionId）
+        SessionContextHolder.setSessionId(sessionId);

-        return answer;
+        try {
+            var response = agent.call(question);
+            long duration = System.currentTimeMillis() - startTime;
+
+            String answer = response.getText();
+
+            // 更新诊断会话
+            session.setStatus("SUCCESS");
+            session.setTotalDurationMs((int) duration);
+            backfillSessionMetrics(session);
+            diagnosisSessionRepository.save(session);
+
+            logger.info("⏱️  总耗时: {} ms", duration);
+            logger.info("📏 输出长度: {} 字符", answer.length());
+            logger.info("========================================");
+
+            return answer;
+        } catch (Exception e) {
+            session.setStatus("FAILED");
+            diagnosisSessionRepository.save(session);
+            throw e;
+        } finally {
+            SessionContextHolder.clear();
+        }
+    }
+
+    /**
+     * 根据问题复杂度自动选择执行策略
+     * @param chatModel 聊天模型
+     * @param toolCallbacks 工具回调
+     * @param question 用户问题
+     * @param history 历史消息
+     * @return AI 回复
+     */
+    public String executeChatWithStrategy(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                          String question, List<Map<String, String>> history) throws GraphRunnerException {
+        if (QuestionComplexity.isComplex(question)) {
+            logger.info("📊 问题判定为复杂，使用多 Agent（Planner + Executor）执行");
+            return executeChatComplex(chatModel, toolCallbacks, question, history);
+        } else {
+            logger.info("📊 问题判定为简单，使用单 Agent 执行");
+            String systemPrompt = buildSystemPrompt(history);
+            ReactAgent agent = createReactAgent(chatModel, systemPrompt);
+            return executeChat(agent, question);
+        }
+    }
+
+    /**
+     * 多 Agent 复杂对话执行（Planner + Executor + Supervisor）
+     */
+    public String executeChatComplex(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                     String question, List<Map<String, String>> history) throws GraphRunnerException {
+        String sessionId = UUID.randomUUID().toString().substring(0, 8);
+        long startTime = System.currentTimeMillis();
+
+        DiagnosisSession session = DiagnosisSession.builder()
+                .sessionId(sessionId)
+                .query(question)
+                .status("RUNNING")
+                .agentFlow("CHAT")
+                .build();
+        diagnosisSessionRepository.save(session);
+
+        SessionContextHolder.setSessionId(sessionId);
+
+        try {
+            ReactAgent planner = buildChatPlannerAgent(chatModel, toolCallbacks, history);
+            ReactAgent executor = buildChatExecutorAgent(chatModel, toolCallbacks, history);
+
+            SupervisorAgent supervisor = SupervisorAgent.builder()
+                    .name("chat_supervisor")
+                    .description("负责调度 Planner 与 Executor 的多 Agent 控制器")
+                    .model(chatModel)
+                    .systemPrompt("你是一个智能任务调度器。分析用户问题，调用 Planner 拆解步骤，调用 Executor 执行各步骤。")
+                    .subAgents(List.of(planner, executor))
+                    .build();
+
+            Optional<OverAllState> stateOptional = supervisor.invoke(question);
+            long duration = System.currentTimeMillis() - startTime;
+
+            String answer = null;
+            if (stateOptional.isPresent()) {
+                // 从 state 中提取 Executor 的最终输出
+                OverAllState state = stateOptional.get();
+                Optional<AssistantMessage> executorOutput = state.value("executor_feedback")
+                        .filter(AssistantMessage.class::isInstance)
+                        .map(AssistantMessage.class::cast);
+                if (executorOutput.isPresent()) {
+                    answer = executorOutput.get().getText();
+                }
+            }
+
+            if (answer == null || answer.isBlank()) {
+                answer = "抱歉，多 Agent 分析未能生成有效结论。";
+            }
+
+            session.setStatus("SUCCESS");
+            session.setTotalDurationMs((int) duration);
+            backfillSessionMetrics(session);
+            diagnosisSessionRepository.save(session);
+
+            logger.info("⏱️  多 Agent 总耗时: {} ms", duration);
+            logger.info("📏 输出长度: {} 字符", answer.length());
+
+            return answer;
+
+        } catch (Exception e) {
+            session.setStatus("FAILED");
+            diagnosisSessionRepository.save(session);
+            logger.error("多 Agent 执行失败", e);
+            return "执行失败: " + e.getMessage();
+        } finally {
+            SessionContextHolder.clear();
+        }
+    }
+
+    private ReactAgent buildChatPlannerAgent(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                             List<Map<String, String>> history) {
+        StringBuilder prompt = new StringBuilder(chatPlannerPrompt);
+        if (!history.isEmpty()) {
+            prompt.append("\n\n--- 对话历史 ---\n");
+            for (Map<String, String> msg : history) {
+                prompt.append(msg.get("role")).append(": ").append(msg.get("content")).append("\n");
+            }
+            prompt.append("--- 对话历史结束 ---\n");
+        }
+        return ReactAgent.builder()
+                .name("chat_planner")
+                .description("负责拆解问题、规划步骤")
+                .model(chatModel)
+                .systemPrompt(prompt.toString())
+                // Planner 不注入工具，只能规划不能执行
+                .hooks(new AgentLoggingHook(agentStepRepository, "planner"))
+                .outputKey("planner_plan")
+                .build();
+    }
+
+    private ReactAgent buildChatExecutorAgent(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                              List<Map<String, String>> history) {
+        StringBuilder prompt = new StringBuilder(chatExecutorPrompt);
+        if (!history.isEmpty()) {
+            prompt.append("\n\n--- 对话历史 ---\n");
+            for (Map<String, String> msg : history) {
+                prompt.append(msg.get("role")).append(": ").append(msg.get("content")).append("\n");
+            }
+            prompt.append("--- 对话历史结束 ---\n");
+        }
+        return ReactAgent.builder()
+                .name("chat_executor")
+                .description("负责执行具体步骤并及时反馈")
+                .model(chatModel)
+                .systemPrompt(prompt.toString())
+                .methodTools(buildMethodToolsArray())
+                .tools(toolCallbacks)
+                .hooks(new AgentLoggingHook(agentStepRepository, "executor"))
+                .outputKey("executor_feedback")
+                .build();
+    }
+
+    /** 从 agent_step 汇总 token、步数等指标回填 diagnosis_session */
+    private void backfillSessionMetrics(DiagnosisSession session) {
+        try {
+            List<com.superbiz.agent.domain.entity.AgentStep> steps =
+                agentStepRepository.findBySessionIdOrderByStepIndex(session.getSessionId());
+
+            if (steps.isEmpty()) return;
+
+            int totalTokens = 0;
+            int stepCount = 0;
+            int toolCallCount = 0;
+            for (var s : steps) {
+                stepCount++;
+                if (s.getTokenCount() != null) totalTokens += s.getTokenCount();
+                if (Boolean.TRUE.equals(s.getHasToolCall())) toolCallCount++;
+            }
+            session.setTotalTokenCount(totalTokens);
+            session.setStepCount(stepCount);
+            session.setToolCallCount(toolCallCount);
+        } catch (Exception e) {
+            logger.warn("回填会话指标失败: sessionId={}", session.getSessionId(), e);
+        }
    }
 }